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veto_council_debate

Submit a task to a council of 7 specialist agents who debate and return a verdict (GREEN, YELLOW, RED, or DEADLOCK) with detailed reasoning.

Instructions

Runs the Veto Council — 7 specialist agents debate your task and return a GREEN/YELLOW/RED/DEADLOCK verdict. For full LLM-backed analysis on any platform (Claude Code, Gemini CLI, Codex CLI) with no API keys: (1) call with task only → get instant deterministic result + llm_upgrade.debate_prompt; (2) reason as all 7 agents using the prompt, then call again with agent_responses → get the LLM-backed verdict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe task or decision to debate. Be specific — include approach, tech stack, and constraints.
contextNoOptional: additional context such as codebase state, prior decisions, or constraints.
max_tokensNoOptional: token budget for this operation. Veto estimates output tokens and warns in the response if the estimate exceeds this limit. Logged to usage_log for tracking.
session_idNoOptional: session ID to associate this council outcome with an active session.
strictnessNoCouncil depth. fast: 3 core agents (dev + architect + security), instant. standard: all 7 agents (default). strict: all 7 + Devil's Advocate rebuttal round on the most critical blocker.
project_dirNoOptional: absolute path to the project directory. Veto will auto-read package.json, git diff, and stack info to give the council real project context.
editor_modelNoOptional: override model used for the editing/execution phase (e.g. claude-3-5-haiku).
agent_responsesNoPhase 2 (LLM upgrade): the JSON object you generated by following llm_upgrade.debate_prompt from a previous call. Veto runs the verdict engine on your responses and returns the final LLM-backed verdict.
architect_modelNoOptional: override model used for the architecture/planning phase (e.g. claude-3-7-sonnet).
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false and destructiveHint=false. The description adds that the tool returns a verdict, reads project files (package.json, git diff) when project_dir is provided, uses tokens, and logs usage. It explains the two-phase process and that the first call is deterministic. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the purpose, covering the core functionality in a single paragraph. It could be more structured (e.g., bullet points for the two phases) but remains efficient and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 params, nested objects, no output schema), the description explains the workflow and key features but does not detail the return value structure beyond the verdict types and the llm_upgrade.debate_prompt. For a tool with no output schema, more information about the response format would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description provides context for the two-phase workflow and the agent_responses parameter but does not add significant meaning beyond the schema descriptions for most parameters. The schema already documents all parameters adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it runs a Veto Council of 7 specialist agents that debate a task and return a verdict (GREEN/YELLOW/RED/DEADLOCK). It distinguishes itself from siblings by specifying the multi-agent debate mechanism and the two-phase workflow (deterministic then LLM-backed).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage instructions: call with task only for instant deterministic result, then reason as agents and call again with agent_responses for LLM-backed verdict. It also mentions optional parameters like strictness and project_dir. However, it does not explicitly state when not to use this tool or suggest alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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